{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### figure-level 和 axes-level 的区别\n",
    "relplot(), catplot() 等函数: figure-level\n",
    "返回: seaborn.FacetGrid\n",
    "lineplot(), scatterplot() 等函数: axes-level\n",
    "返回: matplotlib.Axes"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### FacetGrid 方法\n",
    "- facet_axis(row_i, col_j): 获取指定轴\n",
    "- set(\n",
    "    xlim,\n",
    "    ylim,\n",
    "    xlabel,\n",
    "    xticklabels,\n",
    "    ylabel,\n",
    "    yticklabels\n",
    "    title):           在每个子图轴上设置属性\n",
    "- set_axis_labels():  设置轴标签\n",
    "- set_titles():       绘制标题\n",
    "- set_xlabels():      x轴标签\n",
    "- set_xticklabels():  x轴刻度标签\n",
    "- set_ylabels():      y轴标签\n",
    "- set_yticklabels():  y轴刻度标签\n",
    "#### FacetGrid 属性\n",
    "- ax:   单轴\n",
    "- axes: 多轴(axes.flat)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "   total_bill   tip     sex smoker  day    time  size\n0       16.99  1.01  Female     No  Sun  Dinner     2\n1       10.34  1.66    Male     No  Sun  Dinner     3\n2       21.01  3.50    Male     No  Sun  Dinner     3\n3       23.68  3.31    Male     No  Sun  Dinner     2\n4       24.59  3.61  Female     No  Sun  Dinner     4",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>total_bill</th>\n      <th>tip</th>\n      <th>sex</th>\n      <th>smoker</th>\n      <th>day</th>\n      <th>time</th>\n      <th>size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>16.99</td>\n      <td>1.01</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10.34</td>\n      <td>1.66</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21.01</td>\n      <td>3.50</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23.68</td>\n      <td>3.31</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.59</td>\n      <td>3.61</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 12
    }
   ],
   "source": [
    "tips = sns.load_dataset('tips')\n",
    "tips.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 244 entries, 0 to 243\nData columns (total 7 columns):\ntotal_bill    244 non-null float64\ntip           244 non-null float64\nsex           244 non-null category\nsmoker        244 non-null category\nday           244 non-null category\ntime          244 non-null category\nsize          244 non-null int64\ndtypes: category(4), float64(2), int64(1)\nmemory usage: 7.2 KB\n"
     ],
     "output_type": "stream"
    }
   ],
   "source": [
    "tips.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x2a67ff16550>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 14
    },
    {
     "data": {
      "text/plain": "<Figure size 720x720 with 4 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(\n",
    "    style='whitegrid', # {darkgrid, whitegrid, dark, white, ticks}\n",
    ")\n",
    "# 使用返回值，后期进行调试\n",
    "g = sns.relplot(\n",
    "    kind='line',\n",
    "    x='day',\n",
    "    y='total_bill',\n",
    "    col='sex',\n",
    "    row='time',\n",
    "    data=tips,\n",
    "    ci=None # 关闭置信区间\n",
    ")\n",
    "# 对 多轴 g.axes 展平后进行遍历\n",
    "# for i, ax in enumerate(g.axes.flat):\n",
    "#     ax.set_title(str(i)) # 对每个轴设置标题\n",
    "# 只对（1，0）维度的图设置title\n",
    "# ax = g.facet_axis(row_i=1, col_j=0)\n",
    "# ax.set_title('AAA')\n",
    "g.set(ylim=[10, 30])\n",
    "g.set_titles('AAA')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(\n",
    "    x='day',\n",
    "    y='total_bill',\n",
    "    data=tips,\n",
    "    ci=None\n",
    ")\n",
    "# ax.set_xlabel('xxx')\n",
    "# ax.set_title('BBB')\n",
    "# sns.scatterplot(\n",
    "#     x='day',\n",
    "#     y='tip',\n",
    "#     data=tips,\n",
    "#     ci=None,\n",
    "#     ax=ax\n",
    "# )"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}